Kernel-based deep learning for intelligent data analysis
Jianfeng Wang, Daming Pei · 2017
Machine learning, especially neural networks, has attracted more and more attention in the past few decades. With the further research of intelligent algorithms and network structures, machine learning has been widely used in data mining, computer vision, data recognition and classification. Because the target data is nonlinear and complex, the research needs to extract accurate feature space from the data space. This process relies on machine learning to perform better because manual rules do not achieve the most efficient functions. The researchers combine the kernel approach with the deep neural network to maintain their advantages and compensate for their defects, and then apply depth kernel learning to improve the performance of the algorithm. In this paper, we present an overview of the progress and applications of deep core learning. We introduce the basic theory and their fusion to form several deep core learning structures to improve the performance and performance of the algorithm in practice.